International Journal of Health Care Quality Assurance Improving outpatient phlebotomy service efficiency and patient experience using discrete-event simulation Kenneth Yip Suk-King Pang Kui-Tim Chan Chi-Kuen Chan Tsz-Leung Lee

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Improving outpatient phlebotomy service efficiency and patient experience using discrete-event simulation

Discrete-event simulation

Kenneth Yip

Received 5 August 2015 Revised 17 February 2016 14 March 2016 Accepted 31 May 2016

Finance Division, Hospital Authority, Kowloon, Hong Kong Downloaded by Cornell University Library At 14:01 29 August 2016 (PT)

Suk-King Pang and Kui-Tim Chan

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Specialist Outpatient Department, Queen Mary Hospital, Pok Fu Lam, Hong Kong

Chi-Kuen Chan Department of Clinical Services, The University of Hong Kong – Shenzhen Hospital, Shenzhen, China, and

Tsz-Leung Lee Hong Kong Children’s Hospital, Kowloon, Hong Kong Abstract Purpose – The purpose of this paper is to present a simulation modeling application to reconfigure the outpatient phlebotomy service of an acute regional and teaching hospital in Hong Kong, with an aim to improve service efficiency, shorten patient queuing time and enhance workforce utilization. Design/methodology/approach – The system was modeled as an inhomogeneous Poisson process and a discrete-event simulation model was developed to simulate the current setting, and to evaluate how various performance metrics would change if switched from a decentralized to a centralized model. Variations were then made to the model to test different workforce arrangements for the centralized service, so that managers could decide on the service’s final configuration via an evidence-based and data-driven approach. Findings – This paper provides empirical insights about the relationship between staffing arrangement and system performance via a detailed scenario analysis. One particular staffing scenario was chosen by manages as it was considered to strike the best balance between performance and workforce scheduled. The resulting centralized phlebotomy service was successfully commissioned. Practical implications – This paper demonstrates how analytics could be used for operational planning at the hospital level. The authors show that a transparent and evidence-based scenario analysis, made available through analytics and simulation, greatly facilitates management and clinical stakeholders to arrive at the ideal service configuration. Originality/value – The authors provide a robust method in evaluating the relationship between workforce investment, queuing reduction and workforce utilization, which is crucial for managers when deciding the delivery model for any outpatient-related service. Keywords Service efficiency, Resource allocation, Simulation, Outpatient phlebotomy Paper type Research paper

Introduction Outpatient queuing time generates much attention around the world. Longer waiting times are associated with lower patient satisfaction (Anderson et al., 2007; Leddy et al., 2003). Often, an operational inefficiency symptom, various efforts have been made to improve efficiency and patient flow (Cote 1999; Helbig et al., 2009). The issue is

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Table I. Clinic location

important in Hong Kong, where significant population growth and aging cause a major imbalance between healthcare demand and supply (Mok, 2001; Lloyd-Sherlock, 2000). This queuing problem is apparent in Hong Kong’s specialist outpatient departments (SOPD), where queuing could broadly be divided into payment, phlebotomy, consultation and medication, etc. Phlebotomy and consultation queuing time is significant in most SOPDs. Although there is abundant literature on reducing consultation queuing time using better scheduling, resource allocation, patient flow, etc. (Harper and Gamlin, 2003; Liu and Liu, 1998), only a few attempts have been made to reduce phlebotomy queuing time. Consequently, we use simulation modeling to improve phlebotomy services in a Hong Kong tertiary/quaternary referral and teaching hospital SOPD. Phlebotomy, also known as venipuncture, is a common invasive medical procedure. By obtaining intravenous access, blood sampling and intravenous therapy is performed on patients. In SOPDs, phlebotomy is mainly carried out to obtain blood for diagnostic purposes and to monitor blood components. Queen Mary Hospital (QMH), established in 1937, is a large acute regional hospital in Hong Kong. It is also a teaching hospital (Hong Kong University Li Ka Shing Faculty of Medicine). Owing to QMH’s nature as a tertiary/quaternary center, the QMH SOPD, with 400,000 attendances annually, is also among Hong Kong’s biggest SOPDs. Over one-third of SOPD attendances require phlebotomy services, either about one week before (~50 percent), immediately before (~25 percent) or immediately after (~25 percent) their consultations. The SOPD phlebotomy service was decentralized, where staff in five specialist outpatient clinics (located on six different floors in the same block (Table I), ran their own phlebotomy service independently, employing a conventional fixed-phlebotomist phlebotomy system (CFPPS) approach that Jeon et al. (2010) described. Each clinic had a designated consultation room – a blood-taking room. Healthcare assistants, hired separately in each clinic as phlebotomists, are employed to provide phlebotomy services to the clinic’s patients. Owing to the sporadic nature of patient arrivals, nurses on each floor help the phlebotomists on their individual floors when demand is high (i.e. when long queues form in each clinic). With average phlebotomy queuing time approaching an hour, long queues were a common complaint. Fewer consultation rooms, having one dedicated phlebotomy room per clinic, was also deemed wasted space. Another operational issue with the decentralized service had to do with patient flow. About 50 percent of the blood-taking encounters are for patients without a same-day appointment (i.e. patients need to have their blood sampled about a week prior to their SOPD appointment, so that blood results will be available for their next consultation). These patients need to travel to their clinic via already-crowded elevators, just to get their blood sampled. This severely impacts vertical transportation in the SOPD tower, which was already saturated. Patient flow and congestion issues, with an average 5.4 percent annual increase in service volume over five years in the QMH’s SOPD, it was evident for QMH’s managers that efficiencies Floor

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Medicine, orthopaedics and traumatology Medicine Obstetrics and gynaecology Surgery Ear, nose and throat

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would need to be made to the department’s phlebotomy service to cope with increasing demand. Managers were specifically interested to see whether any new service configuration model could be used so that patient throughput could be increased without injecting any new, recurrent resources. In particular, it was suspected that patient queuing time could be reduced and that workforce and space utilization could be more efficient, if managers switched to a centralized model. Our study was initiated to: evaluate current phlebotomy services, with main performance metrics being patient queuing time, queue length and phlebotomist utilization; analyze whether and in what magnitude centralization would enhance performance; and recommend an ideal service configuration; i.e., how many phlebotomists would be needed at what time. Outpatient phlebotomy service efficiency has been researched. Different efficiency and service enhancement strategies have been proposed; e.g., a new service delivery model was proposed by Culpepper et al. (2006) with improving patient satisfaction as its goal. Melanson et al. (2009) applied Lean management to improve phlebotomy workflow. Differences between a CFPPS vs an active-phlebotomist phlebotomy system was thoroughly discussed by Jeon et al. (2010). Our study’s heart, to analyze whether and how a centralized setting enhances service efficiency and patient queuing time, was also discussed by Mannion and Nadder (2006), where a centralized setting was found to be superior from an efficiency perspective. Moreover, a few attempts had been made to show how simulation modeling could enhance outpatient phlebotomy service efficiency (Woo et al., 2014; Groothuis et al., 2002; Chen et al., 2010). However, centralization benefits in and the exact centralized service configuration depends on the system’s specific demand and supply characteristics; e.g., patient profile, demand pattern, etc. We present a robust method to evaluate the relationship between workforce investment, queue reduction and workforce utilization. Our objective is to provide insights into how hospital services can be optimized and workforce planning could be conducted using analytics, and how service efficiency and performance could be viewed in workforce investment context. Methodology Field research collected separate blood-taking data in clinics S4-S7, such as patient arrivals, service time and phlebotomist utilization, etc. The S3 ear, nose and throat clinic was omitted as there had only been sporadic phlebotomy demand. Unlike emergency services or general outpatient services, specialist outpatient services do not experience seasonality owing to its strictly elective nature. The weekly clinic schedule and patient quota are fixed and would just be repeated week-after-week throughout the year. Hence, there is no significant difference in case mix, arrival patterns and service delivery in different months. As such, April and June were randomly selected for data collection (patient arrival and service time). Blood-taking services in the clinics operate from 8:30 a.m. to 6:00 p.m. Day and time-specific patient arrival data were collected electronically from the electronic ticketing systems in each clinic. Manual collection was also conducted to validate electronic data accuracy). Figure 1 illustrates patient arrivals and patient demand are stable throughout the day, except before 9:30 a.m., where several patients arrive. This mainly reflects patient preference, as 50 percent could freely choose when to have their blood drawn. The other reason behind the high demand in the early morning is that 25 percent would need to have their blood taken immediately before their consultation. We needed to understand service time distribution (Figure 2). Data from 169 bloodtaking encounters were collected manually (a data collector timed each encounter with a stopwatch) in clinic S6, with an assumption that the service time distribution would be similar across all SOPD clinics. Assuming patients from various clinics were

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analogous is justifiable, as the patient profile (who need blood tests) are similar according to clinicians’ expert opinions. Service time was defined as the time it takes when a patient is called in to when the blood-taking procedure is completed and when the next patient is called. Service time is defined by patient, rather than by specimen. We account for blood-taking service time variation (differences in total specimens drawn, age and patients’ physical condition, etc.) by fitting service time as a log-normal distribution (mean 2.54, standard deviation 1.87 minutes). Taking data as an input, the system was then modeled as an inhomogeneous Poisson process and a discrete-event simulation model was developed to simulate the current setting for validation purposes. As a next step, to evaluate how the system behaves under other staffing configurations, an alternate scenario, where the multiqueue was turned into a single-queue system, with resource pooling, was run using the simulation model to evaluate how the performance metrics would change if switched from a decentralized to a centralized model (Figure 3). After showing managers how centralization yields better performances; i.e., queuing time and resource utilization, variations were then made to the centralized

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Figure 3. High-level simulation model: decentralized vs centralized setting

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Results Our modeling results suggested that centralization was highly desirable. For the same workforce (13,500 worker-minutes per week), the average peak-hour queuing time decreased from 47.5 to 17.7 minutes, while the average peak-hour number queuing dropped from 54.1 to 12.1 (Table II). After confirming that centralization yields better performances, six different staffing scenarios were modeled and the relationships between worker-hours scheduled and the various performance metrics were developed. Figure 4 shows how different

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SOPD Blood-Taking – Comparison of Simulated Scenarios Average Number in Queue vs Scheduled Workforce 25 M = 3 with 10 at 9-10 a.m.

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model to test different workforce arrangements for the centralized service. Upon discussion with SOPD managers, the main performance metrics used to compare different scenarios, include average queue length, average queuing time, average peak-hour queue length, average peak-hour queuing time and phlebotomist utilization. Average queue length was considered the most important; i.e., dealing with congestion has always been among the biggest hospital challenges. This scenario analysis produced the performance metrics under various workforce arrangements, so that managers could decide on the service’s final configuration via an evidence-based and data-driven approach. We conducted the simulation modeling exercise using Rockwell Arena 13.5. In total, 20 replications, each with one simulated week in duration.

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SOPD Blood-Taking – Expected Queue Length by Time of Day Scenario Comparison 5/6/7/8/9/10 Stations from 8:30 a.m. to 10:00 a.m., with 4 afterwards 90

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staffing strategies affect average queue length, a metric that we chose as the most important performance metric. The current state (decentralized) scenario and the basic centralized (aggregating existing workforce) scenario are also plotted on Figure 4. Average queues dropped from 16.9 to 6.8 in the basic centralized setting, using the same workforce, 13,500 worker-minutes per week. This is equivalent to asking all phlebotomists on different floors to report to the same location. The basic centralized setting gives a new baseline. Using this as a reference point, we ran other workforce scenarios to see how different staffing strategies yield different outcomes ( Figure 4); e.g., assuming we have more workers, we tested how the system behaves if we have ten full-time stations (i.e. with ten full-time phlebotomists, or 25,499 worker-minutes per week). In this scenario, the average queue length dropped to 5.8. Assuming we have a limited workforce to run the service, we tried more flexible staffing strategies that match better with time-specific demand; e.g., we develop one scenario where we have only two full-time stations, but having more stations in operation during the peak hours; i.e., ten stations from 8:30 to 10 a.m. and four stations from 10:00 to 11:30 a.m. and 3:00 to 4:00 p. m. This staffing scenario required 9,000 worker-minutes and yielded an average queue of 16.7. We also ran four scenarios in between the two extremes described above ( Figure 4). Together they form a front and a diminishing marginal return on workforce could be observed. This agrees with the basic principle around resource pooling, where the marginal efficiency gain will eventually diminish at some point. It was then evident that there is no golden rule about how large a workforce in which one has to invest; rather, the decision depends on managers’ own sense regarding cost vs benefit. In this application, one staffing scenario (M ¼ 4 with ten phlebotomists from 8:30 to 10:00 in the morning and four from 10 a.m. to 5 p.m.) was chosen by managers as it was considered to strike the best balance between performance and workforce scheduled. This setting, with four full-time phlebotomists working during the day and six additional part-time phlebotomists working before 10 a.m., served as the baseline for further discussion among hospital stakeholders. To understand how the morning peak in demand could be served differently, we ran additional scenarios to vary part-time phlebotomist numbers, the centralized service will have during the morning peak hours; i.e., 8:30 and 10:00 a.m. Figure 5 illustrates the six additional scenarios, with the total available stations during the morning peak hours, ranging from five to ten (i.e. one to six part-time phlebotomists plus four full-time staff).

Figure 5. Average queue length with different station numbers from 8:30 to 10:00 a.m.

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Intuitively, having more part-time phlebotomists has a positive impact on patient queuing. Additionally, Figure 5 shows clearly how the morning peak is handled, which would have a significant impact on the performance during the rest of the day; e.g., if only five stations are operational before 10:00 a.m., then the average queue length in the morning would be over 80 and the queue cannot be absorbed until the early afternoon. On the other hand, if eight to ten stations were available, then the queue would be served before 10:00 a.m. and the system would behave well afterwards. It is in this sense that managers decided to have eight stations operational before 10:00 a.m. and four at other times, as the final service configuration. The centralized phlebotomy service, named the centralized outpatient blood-taking room was successfully commissioned. A post-implementation study revealed that while serving similar patient demands, as the decentralized model, the established centralized service achieved an average six-minute queuing time and an average queue length of eight. Discussion Our study shows how analytics and simulation assists managers in operational planning. Although it is intuitive that centralization and resource pooling increases efficiency for a relatively routine service such as phlebotomy, managers were not able to clearly quantify the potential improvements. More importantly, they were unable to estimate the resource needed to run the centralized service (workforce, physical space, etc.). Indeed, one roadblock to establishing a centralized service had always been insufficient physical space and the assumption that a large floor space was needed for the centralized service. We successfully quantified centralization benefits. Via our scenario analysis, managers were also informed of the cost-benefit relationship and the diminishing marginal return on workforce investment regarding queuing vs staffing. We illustrated how having limited blood-taking stations would yield satisfactory results. More importantly, for the first time, managers understood the space needed for the centralized service; i.e., a space that could house eight to ten blood-taking stations, with a waiting area accommodating 100 in the morning and about 30 in the afternoon. This opened the door for more in-depth discussion among various internal stakeholders and paved the way for centralized service implementation. It is with the analytical result that managers could really work with selected internal stakeholders in seriously looking for floor space for the service. An area in the outpatient pharmacy, located in the SOPD tower first floor, was strategically selected for renovation and setting up the centralized service from an enhanced patient flow perspective. First, by removing minimal number (about 30 seats out of 220) in the pharmacy waiting area, enough space could be made to set up eight permanent blood-taking stations. Second, the pharmacy waiting hall could serve as the waiting area for the blood-taking center as well. As SOPD pharmacy visitors would start visiting the pharmacy after 11:00 a.m. (as consultation typically starts at around 10:30 a.m. and the first patients would arrive at around 11:00 a.m.), the waiting hall’s low occupancy in the morning was a good match for the relatively busy blood-taking center in the morning; also the reason behind managers’ disinterest in smoothing the demand via an appointment system; i.e., it seemed better to just serve as many patients as possible before 11:00 a.m. This location, co-locating with the pharmacy, also mitigates the patient flow challenge described earlier. Patient flow could be greatly enhanced, as 50 percent of the blood-taking

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patients have no same-day appointments. These patients now just visit the centralized blood-taking center on the first floor and then leave the hospital using elevators for patients who need to travel to the clinics in the upper seven floors. Since a further 25 percent will need to have their blood taken after their consultations, which they will need to wait in the pharmacy for around half an hour for their medication anyway, they can now save time by first handing in their prescription before attending the blood-taking center. Establishing the centralized service yielded three main benefits. First, for patients, they now enjoy shorter queuing time, a less congested environment and a streamlined patient flow. Second, for SOPD nursing staff, comparing complaints pre- and post-implementation, revealed that they received less patient complaints owing to queuing and over-crowding. Additionally, since phlebotomy services were away from the clinic, nurses could spend their time on patient care, rather than helping out the blood-taking service during morning peaks. Third, managers were happy to see that one consultation room on each floor can now be converted to doctor consultation purposes. This greatly increased attendances per day, which eases the SOPD backlog problem that staff in every SOPD face in Hong Kong. These results were achieved without additional operating and workforce costs. The workforce used for the service had been unchanged after project implementation (at about 225 worker-hours per week). However, since nurses had now been spared from performing phlebotomy services (solely provided by phlebotomists at the centralized service), total workforce costs were estimated to have reduced by 18 percent. The study has limitations. First, we treated historical patient demand as future demand. As discussed earlier, there has been an average 5.4 percent increase in QMH SOPD attendance. In the study’s next phase, efforts will be made to project future demand using age/gender utilization rates and population forecasts so that adequate short-term and long-term planning could be conducted. Additionally, demand (Figure 1) was assumed to hold true in the future. Further work will be conducted to studying whether any demand-side strategies; i.e., the appointment system, enhances efficiency and workforce utilization. Moreover, patients are the primary entity in this study. We argue that a more appropriate entity is specimen, or even blood tests, as service time would be highly correlated to specimens drawn from patients. However, we feel that patient attendance is more fitting in the sense that queuing time and queue lengths are more relevant from a patient viewpoint. Thus, specimen taking variances are taken into account in the log-normal service time distribution described earlier. Conclusions Implementing a change in service delivery model is often costly and resource consuming. Via our analytical work, a change in service delivery, including service relocation, was facilitated for the QMH SOPD phlebotomy service. Although centralization seemed to be the way forward, managers did not have enough evidence to move forward. The exact resources (physical space and workforce) were not easy to estimate, which led to the impasse despite deteriorating decentralized bloodtaking services in different clinics, manifesting as queuing and efficiency problems. With business analytics and simulation modeling applied, managers were given a tool and evidence to convince important internal stakeholders that centralization enhances efficiency and reduces patient queuing time, and that the new service is feasible in the congested hospital. Operations research methods are useful for conducting operational

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planning at the hospital level. In this application, efficiency savings by centralizing outpatient phlebotomy services was easily quantified and presented to managers prior to implementation. We provide insights into the possible diminishing marginal return on workforce investment from a queuing time reduction perspective, which was crucial for decisions on the revised service configuration and subsequent operational and workforce planning after commissioning the service. Using analytics and simulation, a transparent and evidence-based scenario analysis was conducted with managers to arrive at the ideal centralized service configuration.

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Melanson, S., Goonan, E., Lobo, M., Baum, J., Paredes, J., Santos, K., Gustafson, M. and Tanasijevic, M. (2009), “Applying Lean/Toyota production system principles to improve phlebotomy patient satisfaction and workflow”, American Journal of Clinical Pathology, Vol. 132 No. 6, pp. 914-919. Mok, E. (2001), “Hong Kong healthcare system and its challenges”, Journal of Nursing Administration, Vol. 31 No. 11, pp. 520-523. Woo, Y.S., Kwon, Y.D., Lee, M.K., Cha, Y.J. and Kim, H.R. (2014), “A real-time computer simulation program for reducing outpatients phlebotomy wait time”, Journal of Clinical Laboratory Analysis, Vol. 29 No. 4, pp. 255-258.

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Corresponding author Kenneth Yip can be contacted at: [email protected]

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Improving outpatient phlebotomy service efficiency and patient experience using discrete-event simulation.

Purpose - The purpose of this paper is to present a simulation modeling application to reconfigure the outpatient phlebotomy service of an acute regio...
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